Instructions to use ProbeX/Model-J__ResNet__model_idx_0217 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0217 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0217") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0217") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0217", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3ba14df3f0cd0490692aded5249f092bc3f3799c6713c7626191c6b8e7f1318
- Size of remote file:
- 5.37 kB
- SHA256:
- 0e8e05a3d6a14e79c90d34d4c432b146328eb2e3c9b6210030e151869749c321
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